import tensorflow as tf

x_data = [[1,2], [2,3], [3,1], [4,3], [5,3], [6,2]]
y_data = [[0], [0], [0], [1], [1], [1]]

X = tf.placeholder(tf.float32, shape=[None, 2])
Y = tf.placeholder(tf.float32, shape=[None,1])
W = tf.Variable(tf.random_normal([2, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')

hypothesis = tf.sigmoid(tf.matmul(X,W) + b)
cost = -tf.reduce_mean(Y*tf.log(hypothesis) + (1-Y)*tf.log(1-hypothesis))

train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))

with tf.Session() as ss:
    ss.run(tf.global_variables_initializer())
    for step in range(10001):
        cost_val, _ = ss.run([cost, train], feed_dict={X:x_data, Y:y_data})
        if step % 20 ==0:
            print(step, cost_val)

    h, c, a = ss.run([hypothesis, predicted, accuracy], feed_dict={X:x_data, Y:y_data})
    print("\nHypothesis: ", h, "\nCorrect(Y):", c, "\nAccuracy: ",a)

